| { | |
| "paper_id": "E95-1009", | |
| "header": { | |
| "generated_with": "S2ORC 1.0.0", | |
| "date_generated": "2023-01-19T10:31:51.823735Z" | |
| }, | |
| "title": "Computational dialectology in Irish Gaelic", | |
| "authors": [ | |
| { | |
| "first": "Brett", | |
| "middle": [], | |
| "last": "Kessler", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "[email protected]" | |
| }, | |
| { | |
| "first": "", | |
| "middle": [], | |
| "last": "Kilkenny", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Lough", | |
| "middle": [], | |
| "last": "Attorick", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "", | |
| "middle": [], | |
| "last": "Galway", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "", | |
| "middle": [], | |
| "last": "Doolin", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "", | |
| "middle": [], | |
| "last": "Clare", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "", | |
| "middle": [], | |
| "last": "Fanore", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
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| { | |
| "first": "", | |
| "middle": [], | |
| "last": "Kinvra", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
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| { | |
| "first": "", | |
| "middle": [], | |
| "last": "Coomhola", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Kerry", | |
| "middle": [], | |
| "last": "Cloghane", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Kerry", | |
| "middle": [], | |
| "last": "Kilgarvan", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Kerry", | |
| "middle": [], | |
| "last": "Waterville", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Kerry", | |
| "middle": [], | |
| "last": "Killorglin", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "", | |
| "middle": [], | |
| "last": "Careeny", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "", | |
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| "last": "Bmlymacoda", | |
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| "email": "" | |
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| { | |
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| "last": "Newbridge", | |
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| "email": "" | |
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| { | |
| "first": "", | |
| "middle": [], | |
| "last": "Kilsheelan", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
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| { | |
| "first": "", | |
| "middle": [], | |
| "last": "Wateriord", | |
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| "affiliation": {}, | |
| "email": "" | |
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| { | |
| "first": "", | |
| "middle": [], | |
| "last": "Conakilty", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
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| { | |
| "first": "", | |
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| "last": "Craughwell", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
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| { | |
| "first": "", | |
| "middle": [], | |
| "last": "Connacht", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
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| { | |
| "first": "", | |
| "middle": [], | |
| "last": "Coolea", | |
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| "email": "" | |
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| { | |
| "first": "", | |
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| "last": "Rosmuck", | |
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| "email": "" | |
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| { | |
| "first": "Kerry", | |
| "middle": [], | |
| "last": "Glenflesk", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Mount", | |
| "middle": [], | |
| "last": "Melleray", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
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| { | |
| "first": "", | |
| "middle": [], | |
| "last": "Cornamona", | |
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| "email": "" | |
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| { | |
| "first": "Kerry", | |
| "middle": [], | |
| "last": "Dunquin", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "", | |
| "middle": [], | |
| "last": "Tourmakeady", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "", | |
| "middle": [], | |
| "last": "Mayo", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
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| { | |
| "first": "", | |
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| "last": "Ring", | |
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| "email": "" | |
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| { | |
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| "last": "Laughanbeg", | |
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| "email": "" | |
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| { | |
| "first": "", | |
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| "last": "Emlaghmore", | |
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| "email": "" | |
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| { | |
| "first": "Kerry", | |
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| "last": "Lauragh", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
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| { | |
| "first": "", | |
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| "last": "Kilbaha", | |
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| "affiliation": {}, | |
| "email": "" | |
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| { | |
| "first": "", | |
| "middle": [], | |
| "last": "Moycullen", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "", | |
| "middle": [], | |
| "last": "Sliabh Gcua, Waterford", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "", | |
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| "last": "Kilmovee", | |
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| "affiliation": {}, | |
| "email": "" | |
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| { | |
| "first": "", | |
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| "last": "Goatenbridge", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
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| { | |
| "first": "", | |
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| "last": "Tipperary", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
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| { | |
| "first": "", | |
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| "last": "Colmanstown", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "", | |
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| "last": "Inisheer", | |
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| "affiliation": {}, | |
| "email": "" | |
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| { | |
| "first": "", | |
| "middle": [], | |
| "last": "Glentrasna", | |
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| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "", | |
| "middle": [], | |
| "last": "Letterfrack", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
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| { | |
| "first": "", | |
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| "last": "Angliham", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
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| { | |
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| "last": "Annaghdown", | |
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| "email": "" | |
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| { | |
| "first": "Lough", | |
| "middle": [], | |
| "last": "Nmooey", | |
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| "email": "" | |
| }, | |
| { | |
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| "last": "Sonnagh", | |
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| "email": "" | |
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| { | |
| "first": "C~", | |
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| "last": "Rna", | |
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| "email": "" | |
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| { | |
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| "last": "Louisburgh", | |
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| "email": "" | |
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| { | |
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| "last": "Inishmaan", | |
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| "email": "" | |
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| { | |
| "first": "", | |
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| "last": "Camderry", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Ceathrtl", | |
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| "last": "An Tmrbh", | |
| "suffix": "", | |
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| "email": "" | |
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| { | |
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| "last": "Roscommon", | |
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| { | |
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| "last": "Carnmore", | |
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| "email": "" | |
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| { | |
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| "last": "Ballycastle", | |
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| "last": "Cashel", | |
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| { | |
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| "last": "Tobercurry", | |
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| { | |
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| "last": "Sligo", | |
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| { | |
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| "last": "Letterm&caward", | |
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| "last": "Kingarroo", | |
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| "last": "Ceathrd An Tairbh", | |
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| "first": "Emla", | |
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| { | |
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| "last": "Peninsula", | |
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| "first": "Dursey", | |
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| "last": "Sound", | |
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| "abstract": "Dialect groupings can be discovered objectively and automatically by cluster analysis of phonetic transcriptions such as those found in a linguistic atlas. The first step in the analysis, the computation of linguistic distance.between each pair of sites, can be computed as Levenshtein distance between phonetic strings. This correlates closely with the much more laborious technique of determining and counting isoglosses, and is more accurate than the more familiar metric of computing Hamming distance based on whether vocabulary entries match. In the actual clustering step, traditional agglomerative clustering works better thaxl the top-down technique of partitioning around medoids. When agglomerative clustering of phonetic string comparison distances is applied to Gaelic, reasonable dialect boundaries are obtained, corresponding to national and (within Ireland) provincial boundaries.", | |
| "pdf_parse": { | |
| "paper_id": "E95-1009", | |
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| "abstract": [ | |
| { | |
| "text": "Dialect groupings can be discovered objectively and automatically by cluster analysis of phonetic transcriptions such as those found in a linguistic atlas. The first step in the analysis, the computation of linguistic distance.between each pair of sites, can be computed as Levenshtein distance between phonetic strings. This correlates closely with the much more laborious technique of determining and counting isoglosses, and is more accurate than the more familiar metric of computing Hamming distance based on whether vocabulary entries match. In the actual clustering step, traditional agglomerative clustering works better thaxl the top-down technique of partitioning around medoids. When agglomerative clustering of phonetic string comparison distances is applied to Gaelic, reasonable dialect boundaries are obtained, corresponding to national and (within Ireland) provincial boundaries.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Abstract", | |
| "sec_num": null | |
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| ], | |
| "body_text": [ | |
| { | |
| "text": "Defining dialects is one of the first tasks that linguists need to pursue when approaching a language. Knowing the dialect areas helps one allocate resources in language research and has implications for language learners, publishers, broadcasters, educators, and language planners. Unfortunately, dialect definition can be a timeconsuming and ill-defined process. The traditional approach has been to plot isoglosses, delineating regions where the same word is used for the same concept, or perhaps the same pronunciation for the same phoneme. But isoglosses are frustrating. The first problem, as Gaston Paris noted (apud Durand, 1889:49) , is that isoglosses rarely coincide. At best, isoglosses for different features approach each other, forming vague bundles; at *I thank Martin Kay, Paul Kiparsky, Tom Wasow, and a reviewer for helpful comments.", | |
| "cite_spans": [ | |
| { | |
| "start": 624, | |
| "end": 640, | |
| "text": "Durand, 1889:49)", | |
| "ref_id": null | |
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| "section": "Introduction", | |
| "sec_num": "1" | |
| }, | |
| { | |
| "text": "worst, isoglosses may cut across each other, describing completely contradictory binary divisions of the dialect area. That is, language may vary geographically in many dimensions, but the requirements we usually impose require that a specific site be placed in a unique dialect. Traditional dialectological methodology gives little guidance as to how to perform such reduction to one dimension.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Introduction", | |
| "sec_num": "1" | |
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| { | |
| "text": "A second problem is that many isoglosses do not neatly bisect the language area. Often variants do not neatly line up on two sides of a line, but are intermixed haphazardly. More importantly, for some sites information may be lacking, or the question is simply not applicable. When comparing how various sites pronounce the first consonant of a particular word, it is meaningless to ask that question if the site does not use that word. So the isogloss is incomplete and cannot be meaningfully compared with isoglosses based on different sets of sites.", | |
| "cite_spans": [], | |
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| "eq_spans": [], | |
| "section": "Introduction", | |
| "sec_num": "1" | |
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| { | |
| "text": "The third problem is that most languages have dialect continua, such that the speech of one community differs little from the speech of its neighbouts. Even though the cumulative effects of such differences may be great when one considers the ends of the continua (such as southern Italian versus northern French), still it seems arbitrary to draw major dialect boundaries between two villages with very similar speech patterns. Such conundrums led Paris and others to conclude that the dialect boundary, and therefore the very notion of dialect, is an ill-defined concept.", | |
| "cite_spans": [], | |
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| "section": "Introduction", | |
| "sec_num": "1" | |
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| "text": "More recently, the field of dialectometry, as introduced by S~guy (1971, 1973) , has addressed these issues by developing several techniques for summarizing and presenting variation along multiple dimensions. They replace isoglosses with a distance matrix, which compares each site directly with all other sites, ultimately yielding a single figure that measures the linguistic distances between each pair of sites. There is however no firm agreement on just how to compute the distance matrices. Sfiguy's earliest work (1971) was based on lexical correspondences: sites differed in the extent to which they used different words for the same concept. S~guy (1973), Philps (1987) , and Durand (1989) use some combination of lexical, phonological, and morphological data. Babitch (1988) described the dialectal distances in Acadian villages by the degree to which their fishing terminology varied. Babitch and Lebrun (1989) did a similar analysis based on the varying pronunciation of/r/. Elsie (1986) grouped the Gaelic dialects on the basis of whether the vocabulary matched. Ebobisse (1989) grouped the Sawabantu languages of Cameroon by whether phonological correspondences in matching vocabulary items were complete, partial, or lacking. There seems to be a certain bias in favour of working with lexical correspondences, which is understandable, since deciding whether two sites use the same word for the same concept is perhaps one of the easiest linguistic judgements to make. The need to figure out such systems as the comparative phonology of various linguistic sites can be very time-consuming and fraught with arbitrary choices.", | |
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| "start": 60, | |
| "end": 78, | |
| "text": "S~guy (1971, 1973)", | |
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| "start": 520, | |
| "end": 526, | |
| "text": "(1971)", | |
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| "text": "Philps (1987)", | |
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| "end": 698, | |
| "text": "Durand (1989)", | |
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| "end": 784, | |
| "text": "Babitch (1988)", | |
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| "text": "Babitch and Lebrun (1989)", | |
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| "text": "Elsie (1986)", | |
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| "text": "Ebobisse (1989)", | |
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| "text": "Not all dialectometrists agree on the wisdom of delineating dialect areas. Sdguy (1973:18) insisted that the concept of dialect boundaries was meaningless, and his emphasis on the gradience of language similarity has been widely maintained. Bu't those who do look for firna dialect affiliations (such as Babitch and Ebobisse) use bottom-up agglomerative techniques. The two linguistically closest sites are grouped into one dialect, and thenceforth treated as a unit. The process continues recursively until all sites are grouped into one superdialect embracing the entire language area under consideration. This yields a binary tree. But Kaufman and Rousseeuw (1990:44) suggest that when the emphasis in a clustering problem is on the top-level clusters--here, finding the two main dialects--then such bottom-up methods, which can potentially introduce error at each of several steps, are less reliable than top-down partitioning methods. Perhaps past researchers have used inferior bottom-up techniques simply because the necessary algorithms are computationally more tractable. Comparing all possible pairs of sites is a O(N 2) problem, 1 whereas considering all possible two-way partitions of tile dialect area is 0(2 N).", | |
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| "end": 90, | |
| "text": "Sdguy (1973:18)", | |
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| "text": "Kaufman and Rousseeuw (1990:44)", | |
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| "text": "The current state of dialectometry thus presents two main questions which constitute the methodological focus of this paper. The first deals with distance matrices. Is there a way to build accurate distance matrices that minimize editorial decisions without discarding relevant data? My research suggests that this may be done by using string distances computed directly on phonetic transcriptions, and that this is better than restricting the study to lexical comparisons. The second deals with clustering. Do bottom-up or 1The overall algorithm is O(N 3) since for each ue~v group one must compute the distances between it and each of the other sites or groups. top-down techniques work best? My conclusion is that the traditional bottom-up technique works better than a typical top-down method. These conclusions are based partly on an analysis of the mathematical properties of the clusters themselves, partly on how well they correlate with analyses based on more traditional isogloss techniques, and partly on how well they compare with previously-published descriptions of dialects in a specific language, Irish Gaelic.", | |
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| "section": "Introduction", | |
| "sec_num": "1" | |
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| "text": "At one time the Gaelic languag~ group was spoken throughout Ireland, from where it spread to the Isle of Man and to much of Scotland. Currently fully native use of Gaelic is limited to a few discontiguous areas in the westernmost reaches of Ireland and Scotland. In the case of Ireland, everyone agrees that Gaelic is nowadays found in three main dialects: that of Ulster, that of Connacht, and that of Munster (0 Siadhail, 1989) . But several questions are raised that are less easily answered. Do the three provinces separate out so neatly for intrinsic linguistic reasons, or simply because their speakers have become so widely separated from each other geographically as speakers in intervening areas have adopted English? Does the language of Connacht naturally group with that of Ulster or with that of Munster? And looking beyond Ireland, many have commented that the language of Ulster in general is similar to that of Scotland. Are Irish, Manx, and Scottish Gaelic considered three separate languages for intrinsic linguistic reasons, or because they are spoken in different countries? To a large extent, dialectologists have found these questions difficult to answer because they accepted Paris's conundrum. For 6 Siadhail, the ultimate scientific justification ill adopting the three-dialect account is the fact that the Gaeltacht (Irish-speaking territory) is so fragmented nowadays that it no longer forms a continuum. O Cuiv (1951:4-49) felt that there can be no dialect boundaries because transitions are gradual. Elsie (1986:240 ) considers a dialect to be an area where all communities are linguistically more similar to each other than any community is to any site outside the dialect. Such notions provide a very firm, absolute notion of dialecthood: a set of communities either constitutes a dialect area, or it does not. But as the dialectometrists have shown, other notions of clustering are equally scientific and may more accurately correspond to intuitive notions of what it means to be a dialect.", | |
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| "text": "The data for nay study were taken from Wagner 1958. Wagner administered a questionnaire to native speakers of Irish Gaelic in 86 sites. 2 Most of the informants were over seventy years old and 2The atlas also maps for Kilkenny some information gathered from another source. had not spoken Irish since their youth, The atlas is therefore an approximate reconstruction of the linguistic landscape of the turn of the century, when the Gaeltacht was more continuous. Wagner also presents material from the Isle of Man and seven sites in Scotland. The mapped entries are presented in a very narrow phonetic transcription based on the International Phonetic Alphabet.", | |
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| "text": "Volume 1 of Wagner 1958 consists of 300 maps, plotting about 370 concepts. I used the first 51 concepts, or about 4500 different string tokens, as part of an ongoing project to enter all of the atlas into machine readable format. These 51 concepts were represented by 312 different Gaelic words or phrases, whose stems derived from 171 different etymons.", | |
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| }, | |
| { | |
| "text": "3 Methodology and results", | |
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| "text": "To form a baseline for comparison, I analysed the distribution of each of the 51 plotted concepts, finding a total of 3,337 features by which two or more sites differed. For example, for the concept 'sell', I identified two sets, one using the word d(ol (most sites in Ireland), and one using the word creic (Rathlin Island, the Isle of Man, and Scotland). The dialects partitioned in a different way according to how much stress they placed on the verb relative to the pronoun in 'I sold' (even stress in Dunlewy and four of the Scottish sites, else extra stress on the verb). Not all partitions covered the entire map. In this example, only sites that used the word d(ol were compared on the basis of whether a schwa developed in the sequence [i:l]. In some cases the divisions were more than two-way: for example, Wagner distinguishes whether the final consonant in creic is unpalatalized, palatalized, or slightly palatalized. Distance between sites was determined by counting 0 whenever two sites were in the same set and 1 whenever two sites were in contrasting sets, then taking the mean. This baseline approach corresponds formally to determining distance by the number of isoglosses that separate sites, which is in principle the traditional technique.", | |
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| "section": "Distance matrices", | |
| "sec_num": "3.1" | |
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| "text": "This baseline was compared to several other approaches. The etymon identity metric averaged the number of times the sites agreed in using words whose stem had the same ultimate derivation. For example, the dialects differed as to whether they used some form of bullor damhfor the word 'bullock'. Etymon identity is one of the more common approaches in dialectometry; Elsie for example used it in his study of the Gaelic dialects (1986) . Closely related is the idea of word identity, where the words are not counted the same unless all of their morphemes agree. In this analysis, sites that used some form of the word bulldn, with the suffix -dn, were distinguished from those using the suffix -6g.", | |
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| "end": 435, | |
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| "section": "Distance matrices", | |
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| "text": "Another set of approaches for computing distance was based on the phonetics. This computed the Levenshtein distance between phonetic strings. The Levenshtein distance is the cost of the least expensive set of insertions, deletions, or substitutions that would be needed to transform one string into the other (Sankoff and Kruskal, 1983 tie') would get a much larger score, 5.", | |
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| { | |
| "start": 309, | |
| "end": 335, | |
| "text": "(Sankoff and Kruskal, 1983", | |
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| "section": "Distance matrices", | |
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| "text": "In the above technique, very small phonetic differences, such as that between a moderately palatalized and a very palatalized [t], count the same as major differences, such as that between a [t] and an [e]. It would seem to be more accurate to assign a greater distance to substitut.ions involving greater phonetic distinctions. Unfortunately I know of no comprehensive study on the differences between phones, at least not for all 277 contrasts made by Wagner. Instead I distinguished them on the basis of twelve phonetic features that systematically account for all of the distinctions in Wagner's inventory: nasality, stricture, laterality, articulator, glottis, place, palatalization, rounding, length, height, strength, and syllabicity. The features were given discrete ordinal values scaled between 0 and 1, the exact values being arbitrary. For example, place took on the values glottal=O, uvular=0.1, postvelar=0.2, ve-lar=0.3, prevelar=O.~, palatal=0.5, alveolar=0.7, dental=0.8, and labial=l. The distance between any two phones was judged to be the difference between the feature values, averaged across all twelve features. These distances were used instead of uniform 1-unit costs in computing Levenshtein distance. The resulting metric was called feature string comparison.", | |
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| "text": "glottal=O, uvular=0.1, postvelar=0.2, ve-lar=0.3, prevelar=O.~, palatal=0.5, alveolar=0.7, dental=0.8, and labial=l.", | |
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| "text": "It could be argued that it is meaningless to compare the phonetics of different words, as in the case of eallaigh vs. crodh mentioned above. Therefore the feature string comparison was also computed only for pairs of citations that used the same word, so that [AL:i] All of these distance matrices were compared with the isogloss matrix, to see which of them gives results closest to that base method. I used two different methods of comparison, Pearson's p computed between all corresponding cells in the which is a derivative of Kendall's r that Dietz (1983) empirically found particularly accurate as a test statistic for comparing distance matrices, a Table 1 shows that the two measures give parallel results. More importantly, it shows that the approaches based on string comparisons of the phonetic transcriptions correlate more highly with the isogloss approach than do the word or etymon identity measures. Furthermore, comparing whole phone letters works better than the more sophi.sticated technique of comparing features, and restricting comparison to pairs based on the same words does not make the latter any better. Of course, I do not expect that this technique using flat 1-unit costs will prove superior to all methods that are more sensitive to phonetic details. Feature comparison may work better if features were weighted differentially, or if the numeric values they assume were assigned less arbitrarily, or if the Manhattan-style distance computation were replaced by some formula that did not assume that the features are independent of each other. An ideal comparison would be based on data telling how likely it is for the one phone to turn into the other in the course of normal language change. In the method described here, Is] is adjudged closer to [g] than to [h]. But [s] often changes into [hi in the world's languages, and so the pair should have a small distance; whereas the change of [s] to [g] has never occurred to my knowledge, and so should have a very large distance. The unfortunate fact that such ideal data are lacking is compensated for by the fact that the inexpensive phone-string comparison employed in this study performs quite well.", | |
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| "text": "Dietz (1983)", | |
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| "text": "Table 1", | |
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| "section": "Distance matrices", | |
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| "text": "The traditional agglomerative technique for clustering has been described above. There is some 3That is, for each site i, one considers all other pairs of sites, j and k, and asks whether the linguistic difference between i and j is greater or less than that between i and k. One counts 1 if the answer is the same for both distance matrices, -1 if it is different. //'c is the average of these numbers. variation in how the distance between two clusters is measured. For this study I used the average distance between all pairs of elements that are in different clusters. I compared agglomeration to a top-down method that Kaufman and Rousseeuw (1990) call partitioning around medoids. This model reduces the 0(2 Jr) intractability of topdown approaches discussed above by dramatically reducing the number of binary partitions that are considered. Here one seeks to divide the sites into two groups by finding the two representative sites (the medoids) around which all the other sites cluster in such a way as to give the least average distance between the sites and their representatives. This is therefore a O(N 3) algorithm, comparable in efficiency to agglomeration. The process was repeated recursively on each dialect.", | |
| "cite_spans": [ | |
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| "end": 652, | |
| "text": "Kaufman and Rousseeuw (1990)", | |
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| "section": "Clustering techniques", | |
| "sec_num": "3.2" | |
| }, | |
| { | |
| "text": "One way of measuring how well a binary clustering technique works for dialect grouping is to compare for each site i its average dissimilarity from the other sites in the same dialect, a(i), with its average dissimilarity from the sites in the other dialect, b(i). Kaufman and Rousseeuw (1990:83-86 ) define the statistic s(i) to be 1 -a(i)/b(i) if a(i) is less than b(i), otherwise b(i)/a(i) -1. The statistic thus ranges from 1 (perfect fit) to -1 (site i would perfectly fit in the other group). Plotting this statistic gives a silhouelte by which the eye call judge how well classified each site is. Averaging this statistic across all sites gives an idea of how felicitous the overall clustering is, ~.", | |
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| "start": 265, | |
| "end": 298, | |
| "text": "Kaufman and Rousseeuw (1990:83-86", | |
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| "section": "Clustering techniques", | |
| "sec_num": "3.2" | |
| }, | |
| { | |
| "text": "Figures 1-2 present the silhouette for clustering the isogloss distance matrix by partitioning. This analysis produces a large dialect which groups together the sites in Munster, Scotland, the Isle of Man, and almost all sites in Connacht, as well as Rathlin Island in Ulster; and another which groups together all the other sites in Ulster, as well as County Cavan in Connacht. Although the Ulster group is fairly tight, with an g of 0.41, the other group has a more anemic ~ of 0.25, with the sites outside of Munster and Southern Connacht being indifferently classified. The weighted for both groups comes out at 0.29. By comparison, Figures 3-4 show what happens when the traditional agglomerative technique is used. The dialects of Scotland and the Isle of Man form a cluster with a great deal of internal diversity ($ = 0.12), and all the sites in Ireland form another cluster averaging ~ = 0.37, with only Rathlin Island being indifferently classified. The weighted average is 0.35, which is superior to that of the partitioning technique.", | |
| "cite_spans": [], | |
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| { | |
| "start": 637, | |
| "end": 648, | |
| "text": "Figures 3-4", | |
| "ref_id": "FIGREF0" | |
| } | |
| ], | |
| "eq_spans": [], | |
| "section": "Clustering techniques", | |
| "sec_num": "3.2" | |
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| "text": "The same comparative results obtain for almost all of the distance measuring techniques. Table 2 shows that the ~ for the first binary split is usually appreciably higher for agglomeration than it is for partitioning. This result suggests not any inferiority of top-down techniques in general--applying the g statistic to all binary partitions would by Figure 2 : Silhouette for the second dialect grouping computed on the isogloss distance matrix via partitioning. defiuition reveal the optimal split--nor of partitioning around medoids in general. Rather, it appears that the assumption behind this partitioning heuristic, that a site will be closer to the medoid of its own group than to the medoid of the other group, often fails to hold true in dialeetology. The lack of clean breaks between dialects and the fact that dialects of the same language may vary greatly in diameter (i.e., maximal intragroup distances) both mean that the assumption will often be invalid.", | |
| "cite_spans": [], | |
| "ref_spans": [ | |
| { | |
| "start": 89, | |
| "end": 96, | |
| "text": "Table 2", | |
| "ref_id": "TABREF4" | |
| }, | |
| { | |
| "start": 353, | |
| "end": 361, | |
| "text": "Figure 2", | |
| "ref_id": null | |
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| ], | |
| "eq_spans": [], | |
| "section": "Clustering techniques", | |
| "sec_num": "3.2" | |
| }, | |
| { | |
| "text": "Since agglomeration is the better clustering technique, the best dialect analysis should be obtained by agglomerating the isogloss matrix. The best automated approximation should be agglom- differences get smaller, one expects that more data will be required in order to elucidate them.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Gaelic dialects", | |
| "sec_num": "3.3" | |
| }, | |
| { | |
| "text": "This experiment shows that an automatic procedure can reliably group a language into its di-Meet areas, starting from nothing more than phonetic transcriptions as commonly found in linguistic surveys. Accurate distance matrices, corresponding highly to those obtained by the tedious uncovering of thousands of isoglosses, can be obtained by averaging the Levenshtein distance between phonetic strings, weighting equally all insertion, deletion, and substitution operations on the constituent phones. This turns out to be quite a bit more precise than the common technique of measuring distances by judging etymon identity, and requires even less editorial work. That phonetic comparison is more precise is not particularly surprising, since etymon identity ignores a wealth of phonetic, phonological, and morphological data, whereas comparing phones has the side effect of also counting higher-level variation: if words differ in morphemes, their phonetic difference is going to be high. As for clustering the sites into dialect areas, the familiar bottom-up agglomeration method proves superior to top-down partitioning around medoids. Of course simply knowing the dialect areas is not the last word in dialectology. There remain such essential problems as identifying the differing linguistic structures that characterize the dialects, and discovering their history. But all of these tasks will be greatly facilitated by a quick and accurate grouping of the dialects.", | |
| "cite_spans": [], | |
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| "section": "Conclusions", | |
| "sec_num": "4" | |
| } | |
| ], | |
| "back_matter": [], | |
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| "FIGREF0": { | |
| "type_str": "figure", | |
| "text": "Silhouette for isogloss dialect grouping using agglomerative clustering, first group.", | |
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| "text": "Silhouette by agglomeration, Irish group. methods group the Irish sites into one group containing all the sites in Ulster, and another, Southern, group, which itself breaks into a group containing all the sites in Connacht and one containing all the sites in Munster. 4 Both methods agree on how the 87 sites are distributed among these dialects. This three-way division acof the major Gaelic dialects gives the same general results by both distance metrics, if one discounts Manx and Rathlin Island Gaelic, which are flagged as indifferent in both schemes. It is encouraging that the resultant dialect areas are continuous, align with traditional provincial boundaries, and agree with commonly accepted taxonomies. However, dialect groupings at narrower levels, such as the exact subgrouping of the major provincial dialects, are at this point unstable. This is no doubt to be explained by the fairly small number of mapped concepts on which the distance metrics are based (51). 5 As language 4The one site ill Co. Cavan is intermediate between tile Ulster and the Southern group. Wagner also gives two sites in Leinster. The more southern site, in Kilkenny, groups with the Southern group, and the more northern site, in Co. Louth, groups with Ulster, and indeed the county used to be considered part of that province. 5S~guy (1973) cites empirical research suggesting", | |
| "num": null, | |
| "uris": null | |
| }, | |
| "TABREF0": { | |
| "text": "). The simplest technique used was phone string comparison. In this approach, all operations cost 1 unit. Thus in comparing the forms [AL:i] and [aLi] for eallaigh 'cattle', the (minimal) distance was 2, for the substitutions [a]/[A] and [L:]/[L]. (For this measure, diacritics such as the length mark ':' were counted as part of the letter, and different diacritics were adjudged to make for different letters.) A pair of unrelated words like [AL:i] and [khruh] (for crodh, another word for 'cat-", | |
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| "text": "vs. [aLi] would be compared, but [AL:i] vs. [khruh] would be ignored. The different approaches are called all-word vs. same-word", | |
| "content": "<table><tr><td>feature string comparisons.</td></tr></table>", | |
| "html": null, | |
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| "text": "Correlation of distance matrices to the", | |
| "content": "<table><tr><td colspan=\"3\">isogloss distance matrix</td><td/></tr><tr><td/><td/><td/><td>p</td><td>K~</td></tr><tr><td colspan=\"3\">Phone string comparison</td><td colspan=\"2\">.95 .76</td></tr><tr><td colspan=\"3\">Feature string comparison</td><td/></tr><tr><td colspan=\"2\">--</td><td>Ml-word</td><td colspan=\"2\">.92 .70</td></tr><tr><td>-</td><td>-</td><td>same-word</td><td colspan=\"2\">.91 .69</td></tr><tr><td colspan=\"3\">Etymon identity</td><td colspan=\"2\">.85 .61</td></tr><tr><td colspan=\"3\">Word identity</td><td colspan=\"2\">.84 .63</td></tr><tr><td colspan=\"3\">two matrices, and</td><td/></tr><tr><td/><td colspan=\"4\">Kc = Average(sign((Xij -Xik)(Yij -Yik))</td></tr></table>", | |
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| "content": "<table><tr><td>: Statistic g for the top-level binary dialect</td></tr><tr><td>division, comparing partitioning around medoids</td></tr><tr><td>and agglomeration for the different distance ma-</td></tr><tr><td>trices.</td></tr></table>", | |
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